We report a transfusion trial of platelets photochemically treated for pathogen inactivation using the synthetic psoralen amotosalen HCl. Patients with thrombocytopenia were randomly assigned to receive either photochemically treated (PCT) or conventional (control) platelets for up to 28 days. The primary end point was the proportion of patients with World Health Organization (WHO) grade 2 bleeding during the period of platelet support. A total of 645 patients (318 PCT and 327 control) were evaluated. The primary end point, the incidence of grade 2 bleeding (58.5% PCT versus 57.5% control), and the secondary end point, the incidence of grade 3 or 4 bleeding (4.1% PCT versus 6.1% control), were equivalent between the 2 groups (P ؍ .001 by noninferiority). The
Described here are neural networks capable of predicting a drug's mechanism of action from its pattern of activity against a panel of 60 malignant cell lines in the National Cancer Institute's drug screening program. Given six possible classes of mechanism, the network misses the correct category for only 12 out of 141 agents (8.5 percent), whereas linear discriminant analysis, a standard statistical technique, misses 20 out of 141 (14.2 percent). The success of the neural net indicates several things. (i) The cell line response patterns are rich in information about mechanism. (ii) Appropriately designed neural networks can make effective use of that information. (iii) Trained networks can be used to classify prospectively the more than 10,000 agents per year tested by the screening program. Related networks, in combination with classical statistical tools, will help in a variety of ways to move new anticancer agents through the pipeline from in vitro studies to clinical application.
In this study of patients with advanced refractory solid tumors, AMG 706 was well tolerated and displayed favorable pharmacokinetics and evidence of antitumor activity. Additional studies of AMG 706 as monotherapy and in combination with various agents are ongoing.
The National Cancer Institute's drug discovery program screens more than 20,000 chemical compounds and natural products a year for activity against a panel of 60 tumor cell lines in vitro. The result is an information-rich database of patterns that form the basis for what we term an "information-intensive" approach to the process of drug discovery. The first step was a demonstration, both by statistical methods (including the program COMPARE) and by neural networks, that patterns of activity in the screen can be used to predict a compound's mechanism of action. Given this finding, the overall plan has been to develop three large matrices of information: the first (designated A) gives the pattern of activity for each compound tested against each cell line in the screen; the second (S) encodes any of a number of types of 2-D or 3-D structural motifs for each compound; the third (T) indicates each cell's expression of molecular targets (e.g., from 2-dimensional protein gel electrophoresis). Construction and updating of these matrices is an ongoing process. The matrices can be concatenated in various ways to test a variety of specific hypotheses about compounds screened, as well as to "prioritize" candidate compounds for testing. To aid in these efforts, we have developed the DISCOVERY program package, which integrates the matrix data for visual pattern recognition. The "information-intensive" approach summarized here in some senses serves to bridge the perceived gap between screening and structure-based drug design.
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